![]() ![]() In map algebra, the result was rather satisfactory, with 63-67 % agreement between the conventional soil map and maps produced by machine learning. The mapping of soils with varying slopes (LAd, LVAd, CXbd) showed lower accuracy, whereas soils on hydromorphic lowlands (GXbd) were classified more accurately. The eight algorithms showed similar performance without statistical difference (Kappa 0.42-0.48). In a conventional soil survey, the soils described and classified in the Brazilian System of Soil Classification were grouped into composite mapping units (MU) using the conventional method. Mapping performance was assessed using the confusion matrix, and the Z-test among the Kappa indexes of the matrices. ![]() Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine learning algorithms. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. ![]()
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